{"id":"W1960233492","doi":"10.1109/icassp.1988.196780","title":"Estimation of image motion fields: Bayesian formulation and stochastic solution","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Maximum a posteriori estimation; Markov random field; Motion estimation; Random field; Artificial intelligence; Markov chain; Motion field; Computer science; Prior probability; Markov process; Stochastic process; Mathematics; Bayesian probability; Mathematical optimization; Image (mathematics); Machine learning; Image segmentation; Statistics; Maximum likelihood","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001054361,0.00004389671,0.00005136346,0.00005912009,0.00005108649,0.00002716929,0.00004916505,0.00001912355,0.00001044693],"category_scores_gemma":[0.0000856223,0.00004013621,0.00001245631,0.0001055337,0.00001103113,0.0008533117,0.0000195585,0.00002962043,0.000002943861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001072336,"about_ca_system_score_gemma":0.000007793145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000465699,"about_ca_topic_score_gemma":0.000001224928,"domain_scores_codex":[0.999594,0.00001765707,0.0001168235,0.0001141322,0.00008408767,0.00007324998],"domain_scores_gemma":[0.999744,0.0000280717,0.0000507837,0.0001169575,0.00003485048,0.00002535699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002633597,0.00003302766,0.00006163723,0.00002105679,0.000002548717,3.405296e-7,0.000384008,0.01441038,0.005185667,0.2558453,0.00006228495,0.7239911],"study_design_scores_gemma":[0.0001429678,0.00002128204,0.0006187615,0.000009622541,0.000001395521,0.000004562273,0.00001231714,0.9639717,0.00453571,0.0306306,0.00000731818,0.00004370878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002233817,0.00001735797,0.9961334,0.000168512,0.00005557292,0.00007216996,1.173446e-7,0.00004304861,0.001275941],"genre_scores_gemma":[0.6328099,9.921255e-7,0.3671279,0.00003049917,0.000001668055,9.479118e-7,6.436866e-7,0.00000122585,0.00002626593],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9495614,"threshold_uncertainty_score":0.1636707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01058937815170449,"score_gpt":0.2690725991266554,"score_spread":0.2584832209749509,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}